4 citations · 5 across the 7 of their papers we have counts for
7 papers
Parity Supervision as a Driver of Generalization in Quantum Generative Modeling
Markus Baumann, Daniel Hein, Steffen Udluft +3
Generative models learn probability distributions in order to produce new samples beyond a finite training set. Their usefulness therefore depends on assigning probability to valid…
Variational Quantum Circuits in Offline Contextual Bandit Problems
Lukas Schulte, Daniel Hein, Steffen Udluft +1
This paper explores the application of variational quantum circuits (VQCs) for solving offline contextual bandit problems in industrial optimization tasks. Using the Industrial Ben…
From Classical Data to Quantum Advantage -- Quantum Policy Evaluation on Quantum Hardware
Daniel Hein, Simon Wiedemann, Markus Baumann +7
Quantum policy evaluation (QPE) is a reinforcement learning (RL) algorithm which is quadratically more efficient than an analogous classical Monte Carlo estimation. It makes use of…
First Experience with Real-Time Control Using Simulated VQC-Based Quantum Policies
Yize Sun, Mohamad Hagog, Marc Weber +4
This paper investigates the integration of quantum computing into offline reinforcement learning and the deployment of the resulting quantum policy in a real-time control hardware…
Is Q-learning an Ill-posed Problem?
Philipp Wissmann, Daniel Hein, Steffen Udluft +1
This paper investigates the instability of Q-learning in continuous environments, a challenge frequently encountered by practitioners. Traditionally, this instability is attributed…
Why long model-based rollouts are no reason for bad Q-value estimates
Philipp Wissmann, Daniel Hein, Steffen Udluft +1
This paper explores the use of model-based offline reinforcement learning with long model rollouts. While some literature criticizes this approach due to compounding errors, many p…
Model-based Offline Quantum Reinforcement Learning
Simon Eisenmann, Daniel Hein, Steffen Udluft +1
This paper presents the first algorithm for model-based offline quantum reinforcement learning and demonstrates its functionality on the cart-pole benchmark. The model and the poli…